Fiber Tracking in q-Ball Fields Using Regularized Particle Trajectories

• Published in: Information Processing in Medical Imaging Vol. 19; pp. 52 - 63
• Main Authors: Perrin, M., Poupon, C., Cointepas, Y., Rieul, B., Golestani, N., Pallier, C., Rivière, D., Constantinesco, A., Le Bihan, D., Mangin, J. -F.
• Format: Book Chapter Journal Article
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005
• Series: Lecture Notes in Computer Science
• Subjects: Algorithms; Artificial Intelligence; Auditory Tract; Brain - cytology; Diffusion Magnetic Resonance Imaging - methods; Fiber Tracking; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Nerve Fibers, Myelinated - ultrastructure; Orientation Distribution Function; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Tensor Model; Tracking Algorithm
• ISBN: 9783540265450; 3540265457
• ISSN: 0302-9743; 1011-2499; 1611-3349
• DOI: 10.1007/11505730_5

Most of the approaches dedicated to fiber tracking from diffusion-weighted MR data rely on a tensor model. However, the tensor model can only resolve a single fiber orientation within each imaging voxel. New emerging approaches have been proposed to obtain a better representation of the diffusion process occurring in fiber crossing. In this paper, we adapt a tracking algorithm to the q-ball representation, which results from a spherical Radon transform of high angular resolution data. This algorithm is based on a Monte-Carlo strategy, using regularized particle trajectories to sample the white matter geometry. The method is validated using a phantom of bundle crossing made up of haemodialysis fibers. The method is also applied to the detection of the auditory tract in three human subjects.